- Introduction to Generative AI
- What is Generative AI
- Example of Generative AI
- History and Evolution of Generative AI
- Early Developments
- The Rise of GANs
- The Transformer Architecture
- Key Differences from Traditional AI
- Task-Specific versus Creative Outputs
- Supervised versus Unsupervised Learning
- Applications and Implications
- Fundamental Concepts
- Machine Learning Basics
- Types of Machine Learning
- Overview of Neural Networks
- The Elements of a Neural Network Structure
- Key Components
- Types of Neural Networks
- Introduction to Deep Learning
- Features of In-depth Learning
- Popular Architectures
- Deep Learning Applications
- Types of Generative Models
- What are the different Generative AI models
- Generative Adversarial Networks (GANs)
- Variational Autoencoders (VAEs)
- Diffusion Models
- Transformers and Large Language Models
- Transformers
- Structure of Transformers
- Encoder
- Decoder
- The Role of Attention Mechanisms
- Popular Models
- Applications in Natural Language Processing (NLP)
- How LLMs are related to Generative AI?
- Role in Generative AI
- Key Differences and Connections
- Training Generative Models
- Overview
- Data Collection and Preprocessing
- Training Algorithms and Techniques
- Challenges in Training
- Text Generation
- Techniques for Text Generation
- Recurrent Neural Networks (RNNs)
- Long Short-Term Memory Networks (LSTMs)
- Transformers
- Applications in Content Creation
- Ethical Considerations and Limitations
- Image Generation
- Key Techniques for Image Synthesis
- Generative Adversarial Networks (GANs)
- Variational Autoencoders (VAEs)
- Diffusion Models
- Tools and Platforms for Image Generation
- Practical Use Cases of Image Generation
- Video and Animation Generation
- Techniques for Video Synthesis
- Generative Adversarial Networks (GANs)
- Variational Autoencoders (VAEs)
- Diffusion Models
- Emerging Tools and Technologies
- Applications in Media and Entertainment
- Music and Audio Generation
- WaveNet and AudioLM
- Tools for Audio Synthesis
- Use Cases in Creative Industries
- Generative Models for Music
- Recurrent Neural Networks (RNNs)
- Variational Autoencoders (VAEs)
- Transformers
- Code Generation
- Generative AI in Software Development
- Key Benefits of Generative AI in Software Development
- GitHub Copilot
- Features of GitHub Copilot
- Enhancing Productivity and Innovation
- Synthetic Data Generation
- Importance of Synthetic Data
- Methods for Generating Synthetic Data
- Tools for Synthetic Data Generation
- Applications in Research and Development
- Applications of Generative AI in Various Industries
- Applications
- Ethical and Social Implications
- Potential Risks and Challenges
- Addressing Bias and Fairness
- Regulations and Guidelines
- Future Trends in Generative AI
- New Innovations and Technologies
- Predictions for the Next Decade
- The Role of Generative AI in Future AI Developments
- Conclusion
Deep Learning Applications
Computer Vision: Medical image analysis, object detection, facial recognition, and image classification.
Natural Language Processing: Semantic analysis, machine translation, question answering, and text categorization are all examples of natural language processing.
Speech recognition: automatic transcription, language translation, and voice assistants.
Autonomous systems: These include robots, drone navigation, and self-driving automobiles.
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